AI4Life

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Outcomes

Starting its journey in September 2023 and scheduled to conclude in August 2025, the AI4Life project is dedicated to bringing together the life science and computational communities. As we navigate through this timeframe, we are compiling a comprehensive overview of the significant outcomes we’ve achieved so far.

ItemDue DateDescriptionWP
Milestone 130/09/2022First meeting of the Project Management Executive GroupWP1
Deliverable 128/02/2023Report on establishment of project management procedures and executive groupWP1
Deliverable 228/02/2023Strategy for dissemination of information within the consortiumWP1
Deliverable 328/02/2023Risk Management PlanWP1
Deliverable 428/02/2023Data Management PlanWP1
Deliverable 728/02/2023Plan for dissemination and exploitation including communication activitiesWP7
Deliverable 928/02/2023Terms of ServiceWP3
Deliverable 1331/05/2023Notebooks and algorithms repository (accessible from the AI4Life website)WP4
Milestone 231/05/2023Tutorial material availableWP3
Deliverable 1731/08/2023Annotation standards and software, libraries and reference examplesWP5
Deliverable 1931/08/2023First stable BioImage Zoo Model format + software tools, libraries and reference examplesWP5
Milestone 331/08/2023Public release of annotation format and supporting softwareWP5
Milestone 431/08/2023First AI4Life workshop heldWP7
Deliverable 530/09/2023Cloud infrastructure (accessible from the AI4Life website)WP2
Milestone 531/10/2023The computing infrastructure provided for testing among community partners and connected to the website and released to the publicWP2
Milestone 631/12/2023Year 1 Public Challenge for selected open call analysis problems is launched; benchmark implementation availableWP6
Deliverable 1429/02/2024Deployment of the container system of (accessible from the AI4Life website)WP4
Deliverable 1829/02/2024BIA infrastructure and deposition pipeline for versioned reference annotations.WP5
Milestone 729/02/2024BioImage Model Zoo (BMZ) specification and examples released; all models in BMZ to specWP5
Deliverable 630/04/2024Deployment kit for user institutions to replicate the cloud infrastructure (accessible from the AI4Life website)WP2
Milestone 830/04/2024The deployment kit released for power users to set up their own computing infrastructureWP2
Deliverable 730/06/2024Terms of service for the BMZ website and infrastructureWP2
Deliverable 1031/08/2024User-friendly versions of zero-code toolsWP3
Deliverable 1531/08/2024Release of human-in-the-loop interfaces (accessible from the AI4Life website).WP4
Deliverable 2531/08/2024Report on FAIR guidelines followed in the consortium.WP7
Deliverable 831/08/2024Documentation on BioEngine application development (accessible from the AI4Life website)WP2
Milestone 1031/08/2024Release of extensible developer resources promoting contributions to the AI4Life tools repositoryWP4
Milestone 1131/08/2024BMZ standards available on FAIRSharing.orgWP7
Milestone 931/08/2024User-friendly zero-code tools availableWP3
Milestone 1231/12/2024Year 2 Public Challenge for selected open call analysis problems is launched; benchmark implementation availableWP6
Deliverable 1128/02/2025Documentation and training material reportWP3
Deliverable 2028/02/2025Model evaluation platformWP5
Deliverable 2428/02/2025Report on AI4Life workshops, hackathons and communication activities, with other LS RIsWP7
Milestone 1328/02/2025Model benchmarking service activeWP5
Milestone 1430/06/2025Final launch of improved BioImage Model Zoo websiteWP2
Deliverable 1231/08/2025Assessment report of the end-user experienceWP3
Deliverable 1631/08/2025Release resource and data sharing standards (accessible from the AI4Life website)WP4
Deliverable 2131/08/2025Report on all Open Call activities (annually updated and published on the AI4Life website, also available as PDF)WP6
Deliverable 2231/08/2025Report on all Public Challenge activities (annually updated and published on the AI4Life website, also available as PDF)WP6
Milestone 1531/08/2025Year 3 Public Challenge for selected open call analysis problems is launched; benchmark implementation availableWP6
Milestone 1631/08/2025Release of contribution guidelines, ensuring contributions meet standards, quality, and ethical reviewWP4
Deliverable30/11/2023Policy BriefWP1

Publications

Hidalgo-Cenalmor, I., Pylvänäinen, J. W., G. Ferreira, M., Russell, C. T., Saguy, A., Arganda-Carreras, I., Shechtman, Y., AI4Life Horizon Europe Program Consortium, Muñoz-Barrutia, A., Serrano-Solano, B., Barcelo, C. F., Pape, C., Lundberg, E., Jug, F., Deschamps, J., Ferreira, M. G., Hartley, M., Seifi, M., Zulueta-Coarasa, T., … Gómez-de-Mariscal, E. (2024). DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible. Nature Methods. https://doi.org/10.1038/s41592-024-02295-6
Franco-Barranco, D., Andrés-San Román, J. A., Hidalgo-Cenalmor, I., Backová, L., González-Marfil, A., Caporal, C., Chessel, A., Gómez-Gálvez, P., Escudero, L. M., Wei, D., Muñoz-Barrutia, A., & Arganda-Carreras, I. (2024). BiaPy: A unified framework for versatile bioimage analysis with deep learning [Preprint]. bioRxiv. https://doi.org/10.1101/2024.02.03.576026
Gómez-de-Mariscal, E., Del Rosario, M., Pylvänäinen, J. W., Jacquemet, G., & Henriques, R. (2024). Harnessing artificial intelligence to reduce phototoxicity in live imaging. Journal of Cell Science, 137(3), jcs261545. https://doi.org/10.1242/jcs.261545
García López De Haro, C., Dallongeville, S., Musset, T., Gómez-de-Mariscal, E., Sage, D., Ouyang, W., Muñoz-Barrutia, A., Tinevez, J.-Y., & Olivo-Marin, J.-C. (2024). JDLL: a library to run deep learning models on Java bioimage informatics platforms. Nature Methods, 21(1), 7–8. https://doi.org/10.1038/s41592-023-02129-x
Sorensen, L., Humenick, A., Poon, S. S. B., Han, M. N., Mahdavian, N. S., Hamnett, R., Gómez-de-Mariscal, E., Neckel, P. H., Saito, A., Mutunduwe, K., Glennan, C., Haase, R., McQuade, R. M., Foong, J. P. P., Brookes, S. J. H., Kaltschmidt, J. A., Muñoz-Barrutia, A., King, S. K., Veldhuis, N. A., … Rajasekhar, P. (2024). Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons [Preprint]. bioRxiv. https://doi.org/10.1101/2024.01.17.576140
Fuster-Barceló, C., García López De Haro, C., Gómez-de-Mariscal, E., Ouyang, W., Olivo-Marin, J.-C., Sage, D., & Muñoz-Barrutia, A. (2024). Bridging the Gap: Integrating Cutting-edge Techniques into Biological Imaging with deepImageJ [Preprint]. bioRxiv. https://doi.org/10.1101/2024.01.12.575015
Morgado, L., Gómez-de-Mariscal, E., Heil, H. S., & Henriques, R. (2024). The Rise of Data-Driven Microscopy powered by Machine Learning (No. arXiv:2401.05282). arXiv. https://doi.org/10.48550/ARXIV.2401.05282
Pylvänäinen, J. W., Gómez-de-Mariscal, E., Henriques, R., & Jacquemet, G. (2023). Live-cell imaging in the deep learning era. Current Opinion in Cell Biology, 85, 102271. https://doi.org/10.1016/j.ceb.2023.102271
Laine, R. F., Heil, H. S., Coelho, S., Nixon-Abell, J., Jimenez, A., Wiesner, T., Martínez, D., Galgani, T., Régnier, L., Stubb, A., Follain, G., Webster, S., Goyette, J., Dauphin, A., Salles, A., Culley, S., Jacquemet, G., Hajj, B., Leterrier, C., & Henriques, R. (2023). High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation. Nature Methods, 20(12), 1949–1956. https://doi.org/10.1038/s41592-023-02057-w
Spahn, C., Middlemiss, S., Gómez-de-Mariscal, E., Henriques, R., Bode, H. B., Holden, S., & Heilemann, M. (2023). Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth [Preprint]. bioRxiv. https://doi.org/10.1101/2023.10.16.562172
Saraiva, B. M., Cunha, I. M., Brito, A. D., Follain, G., Portela, R., Haase, R., Pereira, P. M., Jacquemet, G., & Henriques, R. (2023). NanoPyx: super-fast bioimage analysis powered by adaptive machine learning [Preprint]. Bioinformatics. https://doi.org/10.1101/2023.08.13.553080
Gómez-de-Mariscal, E., Del Rosario, M., Pylvänäinen, J. W., Jacquemet, G., & Henriques, R. (2023). Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging (No. arXiv:2308.04387). arXiv. https://doi.org/10.48550/arXiv.2308.04387
Kemmer, I., Keppler, A., Serrano-Solano, B., Rybina, A., Özdemir, B., Bischof, J., El Ghadraoui, A., Eriksson, J. E., & Mathur, A. (2023). Building a FAIR image data ecosystem for microscopy communities. Histochemistry and Cell Biology. https://doi.org/10.1007/s00418-023-02203-7
de Haro, C. G. L., Dallongeville, S., Musset, T., de Mariscal, E. G., Sage, D., Ouyang, W., Munoz-Barrutia, A., Tinevez, J.-Y., & Olivo-Marin, J.-C. (2023). JDLL: A library to run Deep Learning models on Java bioimage informatics platforms (No. arXiv:2306.04796). arXiv. https://doi.org/10.48550/arXiv.2306.04796
Maška, M., Ulman, V., Delgado-Rodriguez, P., Gómez-de-Mariscal, E., Nečasová, T., Guerrero Peña, F. A., Ren, T. I., Meyerowitz, E. M., Scherr, T., Löffler, K., Mikut, R., Guo, T., Wang, Y., Allebach, J. P., Bao, R., Al-Shakarji, N. M., Rahmon, G., Toubal, I. E., Palaniappan, K., … Ortiz-de-Solórzano, C. (2023). The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods, 20(7), 1010–1020. https://doi.org/10.1038/s41592-023-01879-y
Volpe, G., Wählby, C., Tian, L., Hecht, M., Yakimovich, A., Monakhova, K., Waller, L., Sbalzarini, I. F., Metzler, C. A., Xie, M., Zhang, K., Lenton, I. C. D., Rubinsztein-Dunlop, H., Brunner, D., Bai, B., Ozcan, A., Midtvedt, D., Wang, H., Sladoje, N., … Bergman, J. (2023). Roadmap on Deep Learning for Microscopy (No. arXiv:2303.03793). arXiv. https://doi.org/10.48550/arXiv.2303.03793
Workshop participants. (2023). AI4Life & BioImage Archive FAIR AI Workshop - Summary of recommendations. https://doi.org/10.5281/zenodo.7681687
Ouyang, W., Eliceiri, K. W., & Cimini, B. A. (2023). Moving beyond the desktop: prospects for practical bioimage analysis via the web. Frontiers in Bioinformatics, 3. https://www.frontiersin.org/articles/10.3389/fbinf.2023.1233748
🦒BioImage.IO Chatbot: A Personalized Assistant for BioImage Analysis Augmented by Community Knowledge Base. (n.d.). https://doi.org/10.5281/zenodo.10032228